Current inductive learning algorithms have difficulties handling attributes with numerical values. This paper presents RULES-F, a new fuzzy inductive learning algorithm in the RULES family, which integrates the capabilities and performance of a good inductive learning algorithm for classification applications with the ability to create accurate and compact fuzzy models for the generation of numerical outputs. The performance of RULES-F in two simulated control applications involving numerical output parameters is demonstrated and compared with that of the well-known fuzzy rule induction algorithm by Wang and Mendel.
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